Returns the whole surface of coefficients, one row per observation and one
column per term, against the single global vector coef() returns
for an lm. That table is the point of fitting a GWR at all:
inspect the spread of a predictor's column to see where, and by how much,
its relationship with the response changes across the study area, and join
it back to object$data_sf to map it. Use
plot.spatial_fit() for a quick look at that map.
Usage
# S3 method for class 'gwr_fit'
coef(object, ...)Value
A data.frame of local coefficient estimates: one row per
observation, one column per model term.
Never NULL: when the engine carries no SDF component this
errors, following the coef() contract described in
new_spatial_fit.
What is and is not returned
Only the model terms: the intercept and one column per predictor.
GWmodel's SDF data slot carries a good deal more alongside them
(standard errors, t-values, the observed response, the fitted values, the
residuals, Local_R2): 15 columns for a two-predictor fit, of which 3
are coefficients. Returning the whole slot would have made
coef(fit)$Local_R2 and coef(fit)$a_SE read like coefficients
and ncol(coef(fit)) a meaningless number. Reach for
object$engine$SDF when you want the rest; it is the unmodified
GWmodel object.
If the model terms cannot be located in the SDF (a GWmodel that
names its coefficient columns differently), the whole slot is returned with
a warning saying so. The call neither errors nor returns a silently short
table.
See also
Other methods on a fitted model:
coef.bayesian_fit(),
coef.rf_fit(),
fitted.bayesian_fit(),
fitted.gwr_fit(),
fitted.rf_fit(),
model_metrics(),
predict.bayesian_fit(),
predict.gwr_fit(),
predict.rf_fit(),
print.rf_fit(),
print.spatial_fit(),
residuals.bayesian_fit(),
residuals.gwr_fit(),
residuals.rf_fit(),
summary.spatial_fit()